Fault Detection in Transmission Towers Using State Observers

2021 ◽  
pp. 774-781
Author(s):  
Gilberto Pechoto de Melo ◽  
Lucas Ferreira Bertão ◽  
Camilo Rocha Araújo
Keyword(s):  
Fault Detection ◽  
Transmission Towers ◽  
State Observers

Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

2021 ◽  
Vol 194 ◽  
pp. 107106
Author(s):  
M.S. Coutinho ◽  
L.R.G.S. Lourenço Novo ◽  
M.T. de Melo ◽  
L.H.A. de Medeiros ◽  
D.C.P. Barbosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Transmission ◽  
Transmission Towers

Actuator Fault Detection and Isolation for Robot Manipulators with the Adaptive Observer

2012 ◽  
Vol 482-484 ◽  
pp. 529-532
Author(s):  
Shao Cong Guo ◽  
Mo Han Yang ◽  
Zi Rui Xing ◽  
Yi Li ◽  
Ji Qing Qiu
Keyword(s):  
Fault Detection ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Experimental Results ◽  
Fault Detection And Isolation ◽  
Adaptive Observer ◽  
Actuator Fault ◽  
Unknown Parameters ◽  
State Observers ◽  
Planar Robot

The fault detection and isolation (FDI) for industrial robot manipulators, subject to faults of actuator, is devised in this paper. An adaptive observer is designed to tackle the robustness problem for unknown parameters due to faults，based on a bank of state observers. By using an adaptive regulating algorithm, the observer is ensured to be stable and the estimated errors are guaranteed to converge. Experimental results are reported for a planar robot under gravity, considering partial failures of the motor torques.

scholarly journals Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

2021 ◽  
Vol vm02 (is01) ◽  
pp. 15-21
Author(s):  
Dinh-dung Nguyen ◽  
Hong Son Tran ◽  
Thi Thuy Tran ◽  
Dat Dang Quoc ◽  
Hong Tien Nguyen
Keyword(s):  
Fault Diagnosis ◽  
Angular Velocity ◽  
Fault Detection ◽  
Intelligent Systems ◽  
Fault Detection And Diagnosis ◽  
Process Models ◽  
State Observers ◽  
Improvement Method ◽  
Detection And Diagnosis ◽  
Sensor Detection

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

Mixed fault diagnosis scheme for satellite formation

2018 ◽  
Vol 90 (2) ◽  
pp. 427-434
Author(s):  
Xiaobin Lian ◽  
Jiafu Liu ◽  
Laohu Yuan ◽  
Naigang Cui
Keyword(s):  
Fault Detection ◽  
Differential Inclusion ◽  
Fault Isolation ◽  
Fault Detection And Diagnosis ◽  
Fault System ◽  
Engineering Practice ◽  
Satellite Formation ◽  
Content Type ◽  
State Observers ◽  
Detection And Diagnosis

Purpose The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis. Design/methodology/approach The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN. Findings The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning. Research limitations/implications The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly. Originality/value This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.

Robust Fault Detection Based on State Observers for Networked Control Systems

2010 ◽  
pp. 346-386
Author(s):  
Zhang-qing Zhu ◽  
Chunlin Chen
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Control Systems ◽  
Networked Control Systems ◽  
Networked Control ◽  
Detection Methods ◽  
State Observers ◽  
Robust Fault Detection ◽  
Uncertain Time ◽  
Short Time

With the development of network and control technology, networked control systems (NCS) have been widely studied recently, especially in the area of complex industrial systems. The system model and states observer based approach is very important for the fault detection (FD) and diagnosis of NCS. This chapter focuses on robust fault detection methods based on states observer. States observers on NCS with short time-delay and uncertain time-delay are both discussed and designed without changing the structure of the systems. The corresponding theorems are systematically given and proved. The methods of robust fault detection on NCS are proposed and some typical examples are demonstrated to test the presented methods.

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